Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Predicting Patient No-Show Using Machine Learning Techniques in the Healthcare Sector

Version 1 : Received: 18 September 2021 / Approved: 20 September 2021 / Online: 20 September 2021 (15:51:54 CEST)

How to cite: Salazar, L.; Leithardt, V.R.Q.; Parreira, W.D.; Fernandes, A.M.R.; Barbosa, J.L.V.; Correia, S.D. Predicting Patient No-Show Using Machine Learning Techniques in the Healthcare Sector. Preprints 2021, 2021090342 (doi: 10.20944/preprints202109.0342.v1). Salazar, L.; Leithardt, V.R.Q.; Parreira, W.D.; Fernandes, A.M.R.; Barbosa, J.L.V.; Correia, S.D. Predicting Patient No-Show Using Machine Learning Techniques in the Healthcare Sector. Preprints 2021, 2021090342 (doi: 10.20944/preprints202109.0342.v1).

Abstract

Today, across the most critical problems faced by hospitals and health centers are those caused by the existence of patients who do not attend their appointments. Among others, this practice generates waste of resources and increases the patients’ waiting list. To handle these problems, hospitals are actively trying to implement methods to reduce the idle time caused by patient no-shows. Many scheduling systems developed require predicting whether a patient will show up for an appointment or not. Although, a challenging problem resides in obtaining these estimates precisely. The goal of this work is to analyze how objective factors influence a patient not to attending their appointment, to identify the main causes that contribute to a patient’s decision, and to be able to predict whether or not the patient will attend the scheduled appointment. As a result, the obtained model is tested on a real dataset collected in a health center linked to the University of Vale do Itajaí (UNIVALI), which includes 25 features and about 5000 samples. The algorithm that produced the best results for the available dataset is the Random Forest classifier. It reveals the best recall rate (0.91), since it measures the ability of a classifier to find all the positive instances and achieves a receiver operating characteristic curve rate of 0.969.

Keywords

Artificial Intelligence; Data Science; HealthCare Applications; Machine Learning; Patient Attitudes

Subject

ENGINEERING, Other

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